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Co-authored-by: Vaibhav Srivastav <reach-vb@users.noreply.huggingface.co>
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ai-modelscope 2025-02-26 23:23:35 +08:00
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--- ---
base_model: Qwen/Qwen2.5-72B-Instruct
language:
- en
library_name: transformers
license: other license: other
license_name: qwen license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4/blob/main/LICENSE license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-72B-Instruct
tags: tags:
- chat - chat
--- ---
@ -50,8 +51,8 @@ Also check out our [GPTQ documentation](https://qwen.readthedocs.io/en/latest/qu
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python ```python
from modelscope import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "qwen/Qwen2.5-72B-Instruct-GPTQ-Int4" model_name = "Qwen/Qwen2.5-72B-Instruct-GPTQ-Int4"
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
model_name, model_name,
torch_dtype="auto", torch_dtype="auto",